人脸生成(Face Generation)

在该项目中,你将使用生成式对抗网络(Generative Adversarial Nets)来生成新的人脸图像。

获取数据

该项目将使用以下数据集:

  • MNIST
  • CelebA

由于 CelebA 数据集比较复杂,而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型,以让你更快的评估所建立模型的性能。

如果你在使用 FloydHub, 请将 data_dir 设置为 "/input" 并使用 FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [3]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Extracting celeba...

探索数据(Explore the Data)

MNIST

MNIST 是一个手写数字的图像数据集。你可以更改 show_n_images 探索此数据集。

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[4]:
<matplotlib.image.AxesImage at 0x7f0f4d7d1940>

CelebA

CelebFaces Attributes Dataset (CelebA) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸,不会用不到相关说明。你可以更改 show_n_images 探索此数据集。

In [5]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[5]:
<matplotlib.image.AxesImage at 0x7f0f4d69f080>

预处理数据(Preprocess the Data)

由于该项目的重点是建立 GANs 模型,我们将为你预处理数据。

经过数据预处理,MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分,然后调整到 28x28 维度。

MNIST 数据集中的图像是单通道的黑白图像,CelebA 数据集中的图像是 三通道的 RGB 彩色图像

建立神经网络(Build the Neural Network)

你将通过部署以下函数来建立 GANs 的主要组成部分:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

检查 TensorFlow 版本并获取 GPU 型号

检查你是否使用正确的 TensorFlow 版本,并获取 GPU 型号

In [6]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

输入(Input)

部署 model_inputs 函数以创建用于神经网络的 占位符 (TF Placeholders)。请创建以下占位符:

  • 输入图像占位符: 使用 image_widthimage_heightimage_channels 设置为 rank 4。
  • 输入 Z 占位符: 设置为 rank 2,并命名为 z_dim
  • 学习速率占位符: 设置为 rank 0。

返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。

In [58]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels))
    input_z = tf.placeholder(tf.float32, (None, z_dim))
    learning_rate = tf.placeholder(tf.float32)
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

辨别器(Discriminator)

部署 discriminator 函数创建辨别器神经网络以辨别 images。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。

该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。

In [137]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha=0.2
    
    with tf.variable_scope("discriminator", reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 128, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x128
        
        x2 = tf.layers.conv2d(relu1, 256, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x256
         
        # Flatten it
        flat = tf.reshape(relu2, (-1, 7*7*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)        
        return out, logits
    
#     with tf.variable_scope("discriminator", reuse=reuse):
#         # Input layer is 28x28x3
#         x1 = tf.layers.conv2d(images, 64, 4, strides=2, padding='same')
#         relu1 = tf.maximum(alpha * x1, x1)
#         # 14x14x32
        
#         x2 = tf.layers.conv2d(relu1, 128, 4, strides=2, padding='same')
#         bn2 = tf.layers.batch_normalization(x2, training=True)
#         relu2 = tf.maximum(alpha * bn2, bn2)
#         # 7x7x128
        
#         x3 = tf.layers.conv2d(relu2, 256, 4, strides=1, padding='valid')
#         bn3 = tf.layers.batch_normalization(x3, training=True)
#         relu3 = tf.maximum(alpha * bn3, bn3)
#         # 4x4x256
    
#         # Flatten it
#         flat = tf.reshape(relu2, (-1, 4*4*256))
#         logits = tf.layers.dense(flat, 1)
#         out = tf.sigmoid(logits)        
#         return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

生成器(Generator)

部署 generator 函数以使用 z 生成图像。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "generator" 的变量空间名来重复使用该函数中的变量。

该函数应返回所生成的 28 x 28 x out_channel_dim 维度图像。

In [138]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha=0.2
    reuse = not is_train
    with tf.variable_scope('generator', reuse=reuse):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*256)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x256 now
        
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x128 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')
        # 28x28xout_channel_dim now
        
        out = tf.tanh(logits)
        return out
    
#         # First fully connected layer
#         x1 = tf.layers.dense(z, 4*4*256)
#         # Reshape it to start the convolutional stack
#         x1 = tf.reshape(x1, (-1, 4, 4, 256))
#         x1 = tf.layers.batch_normalization(x1, training=is_train)
#         x1 = tf.maximum(alpha * x1, x1)
#         # 4x4x256 now
        
#         x2 = tf.layers.conv2d_transpose(x1, 128, 4, strides=1, padding='valid')
#         x2 = tf.layers.batch_normalization(x2, training=is_train)
#         x2 = tf.maximum(alpha * x2, x2)
#         # 7x7x128 now
        
#         x3 = tf.layers.conv2d_transpose(x2, 64, 4, strides=2, padding='same')
#         x3 = tf.layers.batch_normalization(x3, training=is_train)
#         x3 = tf.maximum(alpha * x3, x3)
#         # 14x14x64 now
        
#         # Output layer
#         logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 4, strides=2, padding='same')
#         # 28x28xout_channel_dim now
        
#         out = tf.tanh(logits)
#         return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

损失函数(Loss)

部署 model_loss 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。

使用你已实现的函数:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [139]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    smooth = 0.1
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    g_model = generator(input_z, out_channel_dim, is_train=True)    
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)* (1 - smooth)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

优化(Optimization)

部署 model_opt 函数实现对 GANs 的优化。使用 tf.trainable_variables 获取可训练的所有变量。通过变量空间名 discriminatorgenerator 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。

In [140]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
        
    all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    g_update_ops = [var for var in all_update_ops if var.name.startswith('generator')]
    d_update_ops = [var for var in all_update_ops if var.name.startswith('discriminator')]

    # Optimize
    with tf.control_dependencies(d_update_ops):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    with tf.control_dependencies(g_update_ops):    
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

训练神经网络(Neural Network Training)

输出显示

使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出,这会帮你评估 GANs 模型的训练程度。

In [141]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()
print("done")
done

训练

部署 train 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

使用 show_generator_output 函数显示 generator 在训练过程中的输出。

注意:在每个批次 (batch) 中运行 show_generator_output 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 generator 的输出。

In [142]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, input_learning_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z,  data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, input_learning_rate:learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_learning_rate:learning_rate})
                
                if steps % 20 == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}... steps:{} ".format(epoch_i+1, epochs, steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                if steps % 100 == 0:
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
        print("final Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
        show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
print("done ")   
done 

MNIST

在 MNIST 上测试你的 GANs 模型。经过 2 次迭代,GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失,或接近 0。

In [144]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
print("done")
Epoch 1/2... steps:20  Discriminator Loss: 1.1411... Generator Loss: 2.3109
Epoch 1/2... steps:40  Discriminator Loss: 0.7340... Generator Loss: 1.9546
Epoch 1/2... steps:60  Discriminator Loss: 0.8837... Generator Loss: 1.5440
Epoch 1/2... steps:80  Discriminator Loss: 0.9630... Generator Loss: 1.6523
Epoch 1/2... steps:100  Discriminator Loss: 1.1962... Generator Loss: 2.1909
Epoch 1/2... steps:120  Discriminator Loss: 0.9864... Generator Loss: 1.0819
Epoch 1/2... steps:140  Discriminator Loss: 1.0110... Generator Loss: 0.9266
Epoch 1/2... steps:160  Discriminator Loss: 1.1174... Generator Loss: 0.6957
Epoch 1/2... steps:180  Discriminator Loss: 0.9293... Generator Loss: 1.2418
Epoch 1/2... steps:200  Discriminator Loss: 0.9773... Generator Loss: 0.9833
Epoch 1/2... steps:220  Discriminator Loss: 0.9177... Generator Loss: 1.1203
Epoch 1/2... steps:240  Discriminator Loss: 0.9460... Generator Loss: 1.0221
Epoch 1/2... steps:260  Discriminator Loss: 1.0464... Generator Loss: 2.1939
Epoch 1/2... steps:280  Discriminator Loss: 1.0637... Generator Loss: 2.2039
Epoch 1/2... steps:300  Discriminator Loss: 0.8840... Generator Loss: 1.6429
Epoch 1/2... steps:320  Discriminator Loss: 0.7797... Generator Loss: 1.7854
Epoch 1/2... steps:340  Discriminator Loss: 0.8549... Generator Loss: 1.3483
Epoch 1/2... steps:360  Discriminator Loss: 0.8364... Generator Loss: 1.8040
Epoch 1/2... steps:380  Discriminator Loss: 0.8175... Generator Loss: 1.1312
Epoch 1/2... steps:400  Discriminator Loss: 0.7576... Generator Loss: 1.7847
Epoch 1/2... steps:420  Discriminator Loss: 0.9977... Generator Loss: 0.8341
Epoch 1/2... steps:440  Discriminator Loss: 0.9392... Generator Loss: 0.9320
Epoch 1/2... steps:460  Discriminator Loss: 0.7402... Generator Loss: 1.6008
Epoch 2/2... steps:480  Discriminator Loss: 0.7449... Generator Loss: 1.6698
Epoch 2/2... steps:500  Discriminator Loss: 0.7677... Generator Loss: 1.4818
Epoch 2/2... steps:520  Discriminator Loss: 0.8406... Generator Loss: 1.5776
Epoch 2/2... steps:540  Discriminator Loss: 0.9033... Generator Loss: 1.1479
Epoch 2/2... steps:560  Discriminator Loss: 0.8848... Generator Loss: 1.0826
Epoch 2/2... steps:580  Discriminator Loss: 0.9689... Generator Loss: 1.6779
Epoch 2/2... steps:600  Discriminator Loss: 0.9369... Generator Loss: 1.7409
Epoch 2/2... steps:620  Discriminator Loss: 0.9403... Generator Loss: 1.0600
Epoch 2/2... steps:640  Discriminator Loss: 1.0532... Generator Loss: 0.7993
Epoch 2/2... steps:660  Discriminator Loss: 1.1397... Generator Loss: 1.9252
Epoch 2/2... steps:680  Discriminator Loss: 1.0173... Generator Loss: 1.9073
Epoch 2/2... steps:700  Discriminator Loss: 1.0106... Generator Loss: 1.4751
Epoch 2/2... steps:720  Discriminator Loss: 1.1976... Generator Loss: 2.0771
Epoch 2/2... steps:740  Discriminator Loss: 1.2237... Generator Loss: 0.6345
Epoch 2/2... steps:760  Discriminator Loss: 0.9233... Generator Loss: 1.1284
Epoch 2/2... steps:780  Discriminator Loss: 0.9317... Generator Loss: 1.1331
Epoch 2/2... steps:800  Discriminator Loss: 0.9339... Generator Loss: 1.3860
Epoch 2/2... steps:820  Discriminator Loss: 1.0561... Generator Loss: 1.8322
Epoch 2/2... steps:840  Discriminator Loss: 1.0009... Generator Loss: 0.9139
Epoch 2/2... steps:860  Discriminator Loss: 1.0418... Generator Loss: 0.8057
Epoch 2/2... steps:880  Discriminator Loss: 0.9614... Generator Loss: 1.3285
Epoch 2/2... steps:900  Discriminator Loss: 0.9025... Generator Loss: 1.3362
Epoch 2/2... steps:920  Discriminator Loss: 1.2806... Generator Loss: 0.5998
final Discriminator Loss: 1.2806... Generator Loss: 0.5998
done

CelebA

在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代,或者当 GANs 开始产生真实人脸图像时停止它。

In [146]:
batch_size = 128
z_dim = 200
learning_rate = 0.001
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/10... steps:20  Discriminator Loss: 0.5825... Generator Loss: 19.2827
Epoch 1/10... steps:40  Discriminator Loss: 1.3349... Generator Loss: 6.3147
Epoch 1/10... steps:60  Discriminator Loss: 1.1269... Generator Loss: 3.7735
Epoch 1/10... steps:80  Discriminator Loss: 1.0948... Generator Loss: 0.8390
Epoch 1/10... steps:100  Discriminator Loss: 2.3357... Generator Loss: 0.2468
Epoch 1/10... steps:120  Discriminator Loss: 1.6370... Generator Loss: 0.4933
Epoch 1/10... steps:140  Discriminator Loss: 2.5380... Generator Loss: 0.2371
Epoch 1/10... steps:160  Discriminator Loss: 1.2943... Generator Loss: 4.2466
Epoch 1/10... steps:180  Discriminator Loss: 1.4388... Generator Loss: 5.7985
Epoch 1/10... steps:200  Discriminator Loss: 0.9209... Generator Loss: 7.1618
Epoch 1/10... steps:220  Discriminator Loss: 0.8633... Generator Loss: 1.2728
Epoch 1/10... steps:240  Discriminator Loss: 1.4536... Generator Loss: 0.6102
Epoch 1/10... steps:260  Discriminator Loss: 0.4081... Generator Loss: 4.1121
Epoch 1/10... steps:280  Discriminator Loss: 0.8494... Generator Loss: 1.4697
Epoch 1/10... steps:300  Discriminator Loss: 0.9620... Generator Loss: 4.6375
Epoch 1/10... steps:320  Discriminator Loss: 1.0697... Generator Loss: 4.8602
Epoch 1/10... steps:340  Discriminator Loss: 0.9782... Generator Loss: 1.1473
Epoch 1/10... steps:360  Discriminator Loss: 0.6924... Generator Loss: 1.9459
Epoch 1/10... steps:380  Discriminator Loss: 0.6681... Generator Loss: 1.7978
Epoch 1/10... steps:400  Discriminator Loss: 1.9182... Generator Loss: 2.2725
Epoch 1/10... steps:420  Discriminator Loss: 0.6516... Generator Loss: 1.7416
Epoch 1/10... steps:440  Discriminator Loss: 0.9734... Generator Loss: 1.2657
Epoch 1/10... steps:460  Discriminator Loss: 1.2073... Generator Loss: 1.5551
Epoch 1/10... steps:480  Discriminator Loss: 1.4973... Generator Loss: 0.4704
Epoch 1/10... steps:500  Discriminator Loss: 1.7062... Generator Loss: 4.0714
Epoch 1/10... steps:520  Discriminator Loss: 0.6321... Generator Loss: 2.5238
Epoch 1/10... steps:540  Discriminator Loss: 1.2847... Generator Loss: 2.3369
Epoch 1/10... steps:560  Discriminator Loss: 0.7924... Generator Loss: 1.3151
Epoch 1/10... steps:580  Discriminator Loss: 0.7013... Generator Loss: 1.5853
Epoch 1/10... steps:600  Discriminator Loss: 1.0486... Generator Loss: 1.7155
Epoch 1/10... steps:620  Discriminator Loss: 1.3751... Generator Loss: 0.6387
Epoch 1/10... steps:640  Discriminator Loss: 0.8962... Generator Loss: 1.3303
Epoch 1/10... steps:660  Discriminator Loss: 1.1270... Generator Loss: 1.0620
Epoch 1/10... steps:680  Discriminator Loss: 1.0995... Generator Loss: 3.4450
Epoch 1/10... steps:700  Discriminator Loss: 0.8982... Generator Loss: 2.4577
Epoch 1/10... steps:720  Discriminator Loss: 1.0242... Generator Loss: 0.8603
Epoch 1/10... steps:740  Discriminator Loss: 1.2306... Generator Loss: 0.7805
Epoch 1/10... steps:760  Discriminator Loss: 0.9900... Generator Loss: 3.8159
Epoch 1/10... steps:780  Discriminator Loss: 0.6899... Generator Loss: 1.5176
Epoch 1/10... steps:800  Discriminator Loss: 1.2302... Generator Loss: 1.9273
Epoch 1/10... steps:820  Discriminator Loss: 1.0354... Generator Loss: 1.3902
Epoch 1/10... steps:840  Discriminator Loss: 0.7568... Generator Loss: 2.0024
Epoch 1/10... steps:860  Discriminator Loss: 1.0218... Generator Loss: 1.0740
Epoch 1/10... steps:880  Discriminator Loss: 0.9612... Generator Loss: 1.0316
Epoch 1/10... steps:900  Discriminator Loss: 1.0728... Generator Loss: 1.0243
Epoch 1/10... steps:920  Discriminator Loss: 0.6583... Generator Loss: 3.2459
Epoch 1/10... steps:940  Discriminator Loss: 0.3858... Generator Loss: 3.8144
Epoch 1/10... steps:960  Discriminator Loss: 1.4869... Generator Loss: 0.5904
Epoch 1/10... steps:980  Discriminator Loss: 1.2429... Generator Loss: 1.9367
Epoch 1/10... steps:1000  Discriminator Loss: 1.1411... Generator Loss: 1.3356
Epoch 1/10... steps:1020  Discriminator Loss: 1.2479... Generator Loss: 3.1897
Epoch 1/10... steps:1040  Discriminator Loss: 0.6498... Generator Loss: 1.9535
Epoch 1/10... steps:1060  Discriminator Loss: 0.7887... Generator Loss: 1.6305
Epoch 1/10... steps:1080  Discriminator Loss: 0.9055... Generator Loss: 1.5473
Epoch 1/10... steps:1100  Discriminator Loss: 1.2808... Generator Loss: 2.1770
Epoch 1/10... steps:1120  Discriminator Loss: 1.1770... Generator Loss: 1.3642
Epoch 1/10... steps:1140  Discriminator Loss: 1.0006... Generator Loss: 1.1892
Epoch 1/10... steps:1160  Discriminator Loss: 1.4582... Generator Loss: 1.7012
Epoch 1/10... steps:1180  Discriminator Loss: 0.7452... Generator Loss: 1.7399
Epoch 1/10... steps:1200  Discriminator Loss: 0.8783... Generator Loss: 1.2765
Epoch 1/10... steps:1220  Discriminator Loss: 0.8388... Generator Loss: 2.5234
Epoch 1/10... steps:1240  Discriminator Loss: 0.7749... Generator Loss: 1.3754
Epoch 1/10... steps:1260  Discriminator Loss: 1.2236... Generator Loss: 0.9066
Epoch 1/10... steps:1280  Discriminator Loss: 0.9008... Generator Loss: 1.1767
Epoch 1/10... steps:1300  Discriminator Loss: 1.3754... Generator Loss: 0.6327
Epoch 1/10... steps:1320  Discriminator Loss: 0.8264... Generator Loss: 1.5210
Epoch 1/10... steps:1340  Discriminator Loss: 1.5459... Generator Loss: 0.5031
Epoch 1/10... steps:1360  Discriminator Loss: 1.1862... Generator Loss: 1.8033
Epoch 1/10... steps:1380  Discriminator Loss: 0.5919... Generator Loss: 2.8104
Epoch 1/10... steps:1400  Discriminator Loss: 0.9755... Generator Loss: 1.2523
Epoch 1/10... steps:1420  Discriminator Loss: 0.8594... Generator Loss: 2.5370
Epoch 1/10... steps:1440  Discriminator Loss: 1.2750... Generator Loss: 0.6726
Epoch 1/10... steps:1460  Discriminator Loss: 1.0638... Generator Loss: 1.8072
Epoch 1/10... steps:1480  Discriminator Loss: 0.4523... Generator Loss: 3.6035
Epoch 1/10... steps:1500  Discriminator Loss: 0.7822... Generator Loss: 1.5691
Epoch 1/10... steps:1520  Discriminator Loss: 1.3260... Generator Loss: 3.2939
Epoch 1/10... steps:1540  Discriminator Loss: 0.8214... Generator Loss: 1.5589
Epoch 1/10... steps:1560  Discriminator Loss: 0.8307... Generator Loss: 1.4176
Epoch 1/10... steps:1580  Discriminator Loss: 0.6998... Generator Loss: 2.1907
Epoch 2/10... steps:1600  Discriminator Loss: 0.5665... Generator Loss: 2.4030
Epoch 2/10... steps:1620  Discriminator Loss: 0.7257... Generator Loss: 1.4113
Epoch 2/10... steps:1640  Discriminator Loss: 0.5617... Generator Loss: 2.0139
Epoch 2/10... steps:1660  Discriminator Loss: 1.3544... Generator Loss: 2.6992
Epoch 2/10... steps:1680  Discriminator Loss: 1.1179... Generator Loss: 0.9038
Epoch 2/10... steps:1700  Discriminator Loss: 0.8532... Generator Loss: 2.7552
Epoch 2/10... steps:1720  Discriminator Loss: 0.9463... Generator Loss: 1.3345
Epoch 2/10... steps:1740  Discriminator Loss: 0.7944... Generator Loss: 1.3976
Epoch 2/10... steps:1760  Discriminator Loss: 1.0815... Generator Loss: 1.4342
Epoch 2/10... steps:1780  Discriminator Loss: 1.3702... Generator Loss: 0.5874
Epoch 2/10... steps:1800  Discriminator Loss: 0.8087... Generator Loss: 1.8122
Epoch 2/10... steps:1820  Discriminator Loss: 1.0817... Generator Loss: 0.8825
Epoch 2/10... steps:1840  Discriminator Loss: 1.5184... Generator Loss: 2.3144
Epoch 2/10... steps:1860  Discriminator Loss: 0.7982... Generator Loss: 2.6618
Epoch 2/10... steps:1880  Discriminator Loss: 0.9826... Generator Loss: 1.2797
Epoch 2/10... steps:1900  Discriminator Loss: 0.6123... Generator Loss: 2.9388
Epoch 2/10... steps:1920  Discriminator Loss: 0.9253... Generator Loss: 1.0313
Epoch 2/10... steps:1940  Discriminator Loss: 1.1044... Generator Loss: 1.5360
Epoch 2/10... steps:1960  Discriminator Loss: 0.6722... Generator Loss: 1.9884
Epoch 2/10... steps:1980  Discriminator Loss: 1.0072... Generator Loss: 1.3259
Epoch 2/10... steps:2000  Discriminator Loss: 1.4483... Generator Loss: 1.9931
Epoch 2/10... steps:2020  Discriminator Loss: 0.8588... Generator Loss: 1.2255
Epoch 2/10... steps:2040  Discriminator Loss: 1.0727... Generator Loss: 0.9396
Epoch 2/10... steps:2060  Discriminator Loss: 0.9416... Generator Loss: 0.9994
Epoch 2/10... steps:2080  Discriminator Loss: 1.5263... Generator Loss: 0.4467
Epoch 2/10... steps:2100  Discriminator Loss: 1.3641... Generator Loss: 0.6025
Epoch 2/10... steps:2120  Discriminator Loss: 1.3220... Generator Loss: 0.6388
Epoch 2/10... steps:2140  Discriminator Loss: 0.6919... Generator Loss: 1.9649
Epoch 2/10... steps:2160  Discriminator Loss: 0.6426... Generator Loss: 1.8962
Epoch 2/10... steps:2180  Discriminator Loss: 0.9354... Generator Loss: 1.0427
Epoch 2/10... steps:2200  Discriminator Loss: 0.8987... Generator Loss: 1.1540
Epoch 2/10... steps:2220  Discriminator Loss: 1.0298... Generator Loss: 1.8129
Epoch 2/10... steps:2240  Discriminator Loss: 0.7405... Generator Loss: 1.4873
Epoch 2/10... steps:2260  Discriminator Loss: 1.0900... Generator Loss: 2.2961
Epoch 2/10... steps:2280  Discriminator Loss: 0.7328... Generator Loss: 1.4569
Epoch 2/10... steps:2300  Discriminator Loss: 1.5276... Generator Loss: 1.5956
Epoch 2/10... steps:2320  Discriminator Loss: 1.6672... Generator Loss: 0.8784
Epoch 2/10... steps:2340  Discriminator Loss: 0.8219... Generator Loss: 1.2767
Epoch 2/10... steps:2360  Discriminator Loss: 0.9970... Generator Loss: 0.9945
Epoch 2/10... steps:2380  Discriminator Loss: 1.7333... Generator Loss: 0.4195
Epoch 2/10... steps:2400  Discriminator Loss: 0.6379... Generator Loss: 1.9924
Epoch 2/10... steps:2420  Discriminator Loss: 0.7373... Generator Loss: 1.9038
Epoch 2/10... steps:2440  Discriminator Loss: 0.6896... Generator Loss: 1.5299
Epoch 2/10... steps:2460  Discriminator Loss: 0.7295... Generator Loss: 1.4740
Epoch 2/10... steps:2480  Discriminator Loss: 1.3788... Generator Loss: 2.7186
Epoch 2/10... steps:2500  Discriminator Loss: 1.9417... Generator Loss: 0.2907
Epoch 2/10... steps:2520  Discriminator Loss: 0.6742... Generator Loss: 1.9217
Epoch 2/10... steps:2540  Discriminator Loss: 1.9348... Generator Loss: 0.3134
Epoch 2/10... steps:2560  Discriminator Loss: 1.1579... Generator Loss: 0.8029
Epoch 2/10... steps:2580  Discriminator Loss: 0.7584... Generator Loss: 3.1108
Epoch 2/10... steps:2600  Discriminator Loss: 0.9103... Generator Loss: 1.0874
Epoch 2/10... steps:2620  Discriminator Loss: 0.8490... Generator Loss: 1.4702
Epoch 2/10... steps:2640  Discriminator Loss: 2.2471... Generator Loss: 0.2642
Epoch 2/10... steps:2660  Discriminator Loss: 1.1244... Generator Loss: 0.7465
Epoch 2/10... steps:2680  Discriminator Loss: 0.7381... Generator Loss: 2.6176
Epoch 2/10... steps:2700  Discriminator Loss: 0.6998... Generator Loss: 3.6951
Epoch 2/10... steps:2720  Discriminator Loss: 1.2653... Generator Loss: 0.6645
Epoch 2/10... steps:2740  Discriminator Loss: 0.7905... Generator Loss: 2.9076
Epoch 2/10... steps:2760  Discriminator Loss: 0.8093... Generator Loss: 1.3892
Epoch 2/10... steps:2780  Discriminator Loss: 1.0668... Generator Loss: 0.8084
Epoch 2/10... steps:2800  Discriminator Loss: 0.8239... Generator Loss: 1.3479
Epoch 2/10... steps:2820  Discriminator Loss: 1.4831... Generator Loss: 0.5139
Epoch 2/10... steps:2840  Discriminator Loss: 1.2077... Generator Loss: 0.7083
Epoch 2/10... steps:2860  Discriminator Loss: 0.6812... Generator Loss: 2.1985
Epoch 2/10... steps:2880  Discriminator Loss: 0.7187... Generator Loss: 1.4164
Epoch 2/10... steps:2900  Discriminator Loss: 1.5112... Generator Loss: 1.7626
Epoch 2/10... steps:2920  Discriminator Loss: 1.4430... Generator Loss: 0.5376
Epoch 2/10... steps:2940  Discriminator Loss: 0.6948... Generator Loss: 2.2070
Epoch 2/10... steps:2960  Discriminator Loss: 1.3809... Generator Loss: 0.5523
Epoch 2/10... steps:2980  Discriminator Loss: 0.9729... Generator Loss: 0.9905
Epoch 2/10... steps:3000  Discriminator Loss: 0.6709... Generator Loss: 3.6111
Epoch 2/10... steps:3020  Discriminator Loss: 0.7635... Generator Loss: 1.3749
Epoch 2/10... steps:3040  Discriminator Loss: 1.4115... Generator Loss: 1.2677
Epoch 2/10... steps:3060  Discriminator Loss: 0.5901... Generator Loss: 3.3675
Epoch 2/10... steps:3080  Discriminator Loss: 1.2736... Generator Loss: 1.0513
Epoch 2/10... steps:3100  Discriminator Loss: 0.6611... Generator Loss: 1.8274
Epoch 2/10... steps:3120  Discriminator Loss: 1.5961... Generator Loss: 3.7508
Epoch 2/10... steps:3140  Discriminator Loss: 0.9483... Generator Loss: 1.1060
Epoch 2/10... steps:3160  Discriminator Loss: 1.2584... Generator Loss: 2.0278
Epoch 3/10... steps:3180  Discriminator Loss: 1.1347... Generator Loss: 0.8288
Epoch 3/10... steps:3200  Discriminator Loss: 0.9368... Generator Loss: 1.0609
Epoch 3/10... steps:3220  Discriminator Loss: 0.5770... Generator Loss: 2.4158
Epoch 3/10... steps:3240  Discriminator Loss: 0.5669... Generator Loss: 2.0924
Epoch 3/10... steps:3260  Discriminator Loss: 0.5817... Generator Loss: 2.9000
Epoch 3/10... steps:3280  Discriminator Loss: 0.5868... Generator Loss: 1.8495
Epoch 3/10... steps:3300  Discriminator Loss: 1.0476... Generator Loss: 0.8636
Epoch 3/10... steps:3320  Discriminator Loss: 1.2745... Generator Loss: 1.1774
Epoch 3/10... steps:3340  Discriminator Loss: 1.2849... Generator Loss: 0.6802
Epoch 3/10... steps:3360  Discriminator Loss: 0.9129... Generator Loss: 2.0211
Epoch 3/10... steps:3380  Discriminator Loss: 0.8839... Generator Loss: 1.1767
Epoch 3/10... steps:3400  Discriminator Loss: 1.4846... Generator Loss: 0.5270
Epoch 3/10... steps:3420  Discriminator Loss: 0.7324... Generator Loss: 2.4511
Epoch 3/10... steps:3440  Discriminator Loss: 0.8155... Generator Loss: 1.3147
Epoch 3/10... steps:3460  Discriminator Loss: 1.8392... Generator Loss: 0.3788
Epoch 3/10... steps:3480  Discriminator Loss: 0.8655... Generator Loss: 1.4285
Epoch 3/10... steps:3500  Discriminator Loss: 2.2849... Generator Loss: 0.2289
Epoch 3/10... steps:3520  Discriminator Loss: 0.7844... Generator Loss: 1.6542
Epoch 3/10... steps:3540  Discriminator Loss: 1.7034... Generator Loss: 0.4002
Epoch 3/10... steps:3560  Discriminator Loss: 1.1612... Generator Loss: 2.3014
Epoch 3/10... steps:3580  Discriminator Loss: 1.2145... Generator Loss: 0.8166
Epoch 3/10... steps:3600  Discriminator Loss: 0.8330... Generator Loss: 1.4015
Epoch 3/10... steps:3620  Discriminator Loss: 0.5266... Generator Loss: 2.6060
Epoch 3/10... steps:3640  Discriminator Loss: 0.9117... Generator Loss: 1.0477
Epoch 3/10... steps:3660  Discriminator Loss: 0.8705... Generator Loss: 2.4336
Epoch 3/10... steps:3680  Discriminator Loss: 0.7169... Generator Loss: 1.3894
Epoch 3/10... steps:3700  Discriminator Loss: 1.6551... Generator Loss: 0.5635
Epoch 3/10... steps:3720  Discriminator Loss: 1.0196... Generator Loss: 2.0966
Epoch 3/10... steps:3740  Discriminator Loss: 0.5425... Generator Loss: 2.1975
Epoch 3/10... steps:3760  Discriminator Loss: 0.7955... Generator Loss: 1.6149
Epoch 3/10... steps:3780  Discriminator Loss: 0.4744... Generator Loss: 2.7391
Epoch 3/10... steps:3800  Discriminator Loss: 1.6989... Generator Loss: 0.4592
Epoch 3/10... steps:3820  Discriminator Loss: 0.7111... Generator Loss: 2.9679
Epoch 3/10... steps:3840  Discriminator Loss: 1.4041... Generator Loss: 0.5563
Epoch 3/10... steps:3860  Discriminator Loss: 1.0489... Generator Loss: 0.8874
Epoch 3/10... steps:3880  Discriminator Loss: 0.6423... Generator Loss: 1.6677
Epoch 3/10... steps:3900  Discriminator Loss: 0.6508... Generator Loss: 3.2844
Epoch 3/10... steps:3920  Discriminator Loss: 0.7957... Generator Loss: 1.6220
Epoch 3/10... steps:3940  Discriminator Loss: 0.6053... Generator Loss: 1.9766
Epoch 3/10... steps:3960  Discriminator Loss: 0.9141... Generator Loss: 2.7145
Epoch 3/10... steps:3980  Discriminator Loss: 1.0813... Generator Loss: 2.2487
Epoch 3/10... steps:4000  Discriminator Loss: 0.9958... Generator Loss: 0.9798
Epoch 3/10... steps:4020  Discriminator Loss: 2.4285... Generator Loss: 2.9726
Epoch 3/10... steps:4040  Discriminator Loss: 0.9783... Generator Loss: 0.9870
Epoch 3/10... steps:4060  Discriminator Loss: 1.5513... Generator Loss: 0.5577
Epoch 3/10... steps:4080  Discriminator Loss: 1.2936... Generator Loss: 0.6524
Epoch 3/10... steps:4100  Discriminator Loss: 0.7914... Generator Loss: 3.0549
Epoch 3/10... steps:4120  Discriminator Loss: 0.8996... Generator Loss: 1.1304
Epoch 3/10... steps:4140  Discriminator Loss: 0.6997... Generator Loss: 1.7072
Epoch 3/10... steps:4160  Discriminator Loss: 1.1453... Generator Loss: 0.7805
Epoch 3/10... steps:4180  Discriminator Loss: 2.1756... Generator Loss: 3.1122
Epoch 3/10... steps:4200  Discriminator Loss: 0.7429... Generator Loss: 1.5730
Epoch 3/10... steps:4220  Discriminator Loss: 0.9702... Generator Loss: 1.1455
Epoch 3/10... steps:4240  Discriminator Loss: 1.7162... Generator Loss: 0.4320
Epoch 3/10... steps:4260  Discriminator Loss: 0.5571... Generator Loss: 2.0296
Epoch 3/10... steps:4280  Discriminator Loss: 0.7120... Generator Loss: 1.5273
Epoch 3/10... steps:4300  Discriminator Loss: 1.3940... Generator Loss: 0.5768
Epoch 3/10... steps:4320  Discriminator Loss: 1.0386... Generator Loss: 3.6277
Epoch 3/10... steps:4340  Discriminator Loss: 1.1272... Generator Loss: 0.8329
Epoch 3/10... steps:4360  Discriminator Loss: 1.0913... Generator Loss: 0.9000
Epoch 3/10... steps:4380  Discriminator Loss: 0.8025... Generator Loss: 1.3217
Epoch 3/10... steps:4400  Discriminator Loss: 1.1630... Generator Loss: 0.7719
Epoch 3/10... steps:4420  Discriminator Loss: 1.1432... Generator Loss: 1.3981
Epoch 3/10... steps:4440  Discriminator Loss: 0.9973... Generator Loss: 2.2436
Epoch 3/10... steps:4460  Discriminator Loss: 0.6398... Generator Loss: 1.9936
Epoch 3/10... steps:4480  Discriminator Loss: 0.8139... Generator Loss: 1.3386
Epoch 3/10... steps:4500  Discriminator Loss: 0.5435... Generator Loss: 2.5440
Epoch 3/10... steps:4520  Discriminator Loss: 0.4380... Generator Loss: 3.3239
Epoch 3/10... steps:4540  Discriminator Loss: 2.0000... Generator Loss: 3.5995
Epoch 3/10... steps:4560  Discriminator Loss: 0.8219... Generator Loss: 1.4253
Epoch 3/10... steps:4580  Discriminator Loss: 1.2408... Generator Loss: 2.9348
Epoch 3/10... steps:4600  Discriminator Loss: 0.7098... Generator Loss: 1.8369
Epoch 3/10... steps:4620  Discriminator Loss: 1.8079... Generator Loss: 0.4295
Epoch 3/10... steps:4640  Discriminator Loss: 0.7575... Generator Loss: 1.3947
Epoch 3/10... steps:4660  Discriminator Loss: 0.6729... Generator Loss: 2.6392
Epoch 3/10... steps:4680  Discriminator Loss: 0.8683... Generator Loss: 1.3879
Epoch 3/10... steps:4700  Discriminator Loss: 0.5649... Generator Loss: 2.6500
Epoch 3/10... steps:4720  Discriminator Loss: 0.6471... Generator Loss: 1.8653
Epoch 3/10... steps:4740  Discriminator Loss: 0.8732... Generator Loss: 1.7807
Epoch 4/10... steps:4760  Discriminator Loss: 0.7251... Generator Loss: 2.0448
Epoch 4/10... steps:4780  Discriminator Loss: 2.6323... Generator Loss: 0.1868
Epoch 4/10... steps:4800  Discriminator Loss: 0.9640... Generator Loss: 1.2882
Epoch 4/10... steps:4820  Discriminator Loss: 1.0205... Generator Loss: 2.1429
Epoch 4/10... steps:4840  Discriminator Loss: 1.1698... Generator Loss: 1.7011
Epoch 4/10... steps:4860  Discriminator Loss: 0.7602... Generator Loss: 1.5624
Epoch 4/10... steps:4880  Discriminator Loss: 0.7947... Generator Loss: 1.2910
Epoch 4/10... steps:4900  Discriminator Loss: 1.4873... Generator Loss: 0.5504
Epoch 4/10... steps:4920  Discriminator Loss: 0.9198... Generator Loss: 1.0778
Epoch 4/10... steps:4940  Discriminator Loss: 0.5547... Generator Loss: 2.5548
Epoch 4/10... steps:4960  Discriminator Loss: 0.6434... Generator Loss: 1.7399
Epoch 4/10... steps:4980  Discriminator Loss: 2.2451... Generator Loss: 0.2555
Epoch 4/10... steps:5000  Discriminator Loss: 0.6799... Generator Loss: 1.5606
Epoch 4/10... steps:5020  Discriminator Loss: 0.8142... Generator Loss: 1.2961
Epoch 4/10... steps:5040  Discriminator Loss: 1.9393... Generator Loss: 0.3623
Epoch 4/10... steps:5060  Discriminator Loss: 1.3858... Generator Loss: 0.5757
Epoch 4/10... steps:5080  Discriminator Loss: 0.8084... Generator Loss: 1.9580
Epoch 4/10... steps:5100  Discriminator Loss: 0.9392... Generator Loss: 3.2673
Epoch 4/10... steps:5120  Discriminator Loss: 1.0901... Generator Loss: 0.8952
Epoch 4/10... steps:5140  Discriminator Loss: 0.6215... Generator Loss: 3.5998
Epoch 4/10... steps:5160  Discriminator Loss: 0.8454... Generator Loss: 1.3250
Epoch 4/10... steps:5180  Discriminator Loss: 0.5895... Generator Loss: 2.3331
Epoch 4/10... steps:5200  Discriminator Loss: 0.7070... Generator Loss: 1.6519
Epoch 4/10... steps:5220  Discriminator Loss: 0.4645... Generator Loss: 3.4068
Epoch 4/10... steps:5240  Discriminator Loss: 0.4957... Generator Loss: 3.1015
Epoch 4/10... steps:5260  Discriminator Loss: 0.6588... Generator Loss: 1.6765
Epoch 4/10... steps:5280  Discriminator Loss: 1.3938... Generator Loss: 1.8201
Epoch 4/10... steps:5300  Discriminator Loss: 1.0024... Generator Loss: 1.0441
Epoch 4/10... steps:5320  Discriminator Loss: 0.9910... Generator Loss: 1.6957
Epoch 4/10... steps:5340  Discriminator Loss: 1.2433... Generator Loss: 0.7101
Epoch 4/10... steps:5360  Discriminator Loss: 0.7791... Generator Loss: 1.5401
Epoch 4/10... steps:5380  Discriminator Loss: 1.5843... Generator Loss: 1.6290
Epoch 4/10... steps:5400  Discriminator Loss: 1.0714... Generator Loss: 2.2795
Epoch 4/10... steps:5420  Discriminator Loss: 1.3636... Generator Loss: 1.3241
Epoch 4/10... steps:5440  Discriminator Loss: 0.9106... Generator Loss: 1.1881
Epoch 4/10... steps:5460  Discriminator Loss: 0.9062... Generator Loss: 1.9322
Epoch 4/10... steps:5480  Discriminator Loss: 0.5925... Generator Loss: 2.1480
Epoch 4/10... steps:5500  Discriminator Loss: 1.2766... Generator Loss: 0.6400
Epoch 4/10... steps:5520  Discriminator Loss: 1.3781... Generator Loss: 1.6910
Epoch 4/10... steps:5540  Discriminator Loss: 1.4709... Generator Loss: 0.5357
Epoch 4/10... steps:5560  Discriminator Loss: 0.7322... Generator Loss: 1.4128
Epoch 4/10... steps:5580  Discriminator Loss: 0.7012... Generator Loss: 1.7388
Epoch 4/10... steps:5600  Discriminator Loss: 2.0010... Generator Loss: 0.2972
Epoch 4/10... steps:5620  Discriminator Loss: 0.8263... Generator Loss: 1.8962
Epoch 4/10... steps:5640  Discriminator Loss: 1.2028... Generator Loss: 0.7170
Epoch 4/10... steps:5660  Discriminator Loss: 0.9616... Generator Loss: 1.0447
Epoch 4/10... steps:5680  Discriminator Loss: 0.8400... Generator Loss: 1.3735
Epoch 4/10... steps:5700  Discriminator Loss: 1.0964... Generator Loss: 0.8619
Epoch 4/10... steps:5720  Discriminator Loss: 1.2885... Generator Loss: 0.6656
Epoch 4/10... steps:5740  Discriminator Loss: 0.7477... Generator Loss: 1.7922
Epoch 4/10... steps:5760  Discriminator Loss: 0.8117... Generator Loss: 2.0250
Epoch 4/10... steps:5780  Discriminator Loss: 0.7550... Generator Loss: 1.4494
Epoch 4/10... steps:5800  Discriminator Loss: 0.5592... Generator Loss: 2.3901
Epoch 4/10... steps:5820  Discriminator Loss: 1.0169... Generator Loss: 4.4064
Epoch 4/10... steps:5840  Discriminator Loss: 0.5965... Generator Loss: 2.3080
Epoch 4/10... steps:5860  Discriminator Loss: 1.5187... Generator Loss: 0.4914
Epoch 4/10... steps:5880  Discriminator Loss: 0.8145... Generator Loss: 1.4229
Epoch 4/10... steps:5900  Discriminator Loss: 1.5634... Generator Loss: 0.4952
Epoch 4/10... steps:5920  Discriminator Loss: 2.1950... Generator Loss: 0.2563
Epoch 4/10... steps:5940  Discriminator Loss: 1.0727... Generator Loss: 2.0269
Epoch 4/10... steps:5960  Discriminator Loss: 0.8571... Generator Loss: 1.0792
Epoch 4/10... steps:5980  Discriminator Loss: 1.7699... Generator Loss: 0.4359
Epoch 4/10... steps:6000  Discriminator Loss: 0.7689... Generator Loss: 1.4898
Epoch 4/10... steps:6020  Discriminator Loss: 0.8403... Generator Loss: 1.3681
Epoch 4/10... steps:6040  Discriminator Loss: 0.9482... Generator Loss: 1.8932
Epoch 4/10... steps:6060  Discriminator Loss: 0.6134... Generator Loss: 1.9635
Epoch 4/10... steps:6080  Discriminator Loss: 0.5450... Generator Loss: 2.4284
Epoch 4/10... steps:6100  Discriminator Loss: 0.8390... Generator Loss: 1.3223
Epoch 4/10... steps:6120  Discriminator Loss: 0.7939... Generator Loss: 2.4435
Epoch 4/10... steps:6140  Discriminator Loss: 0.8106... Generator Loss: 1.2482
Epoch 4/10... steps:6160  Discriminator Loss: 1.7524... Generator Loss: 2.5169
Epoch 4/10... steps:6180  Discriminator Loss: 0.6750... Generator Loss: 3.2026
Epoch 4/10... steps:6200  Discriminator Loss: 0.7085... Generator Loss: 1.5317
Epoch 4/10... steps:6220  Discriminator Loss: 0.4914... Generator Loss: 3.1801
Epoch 4/10... steps:6240  Discriminator Loss: 1.3279... Generator Loss: 2.4713
Epoch 4/10... steps:6260  Discriminator Loss: 0.7074... Generator Loss: 3.0868
Epoch 4/10... steps:6280  Discriminator Loss: 0.5077... Generator Loss: 2.3983
Epoch 4/10... steps:6300  Discriminator Loss: 2.3669... Generator Loss: 2.1388
Epoch 4/10... steps:6320  Discriminator Loss: 0.9125... Generator Loss: 1.3657
Epoch 5/10... steps:6340  Discriminator Loss: 0.9468... Generator Loss: 1.5236
Epoch 5/10... steps:6360  Discriminator Loss: 0.5807... Generator Loss: 2.1904
Epoch 5/10... steps:6380  Discriminator Loss: 0.6392... Generator Loss: 1.7783
Epoch 5/10... steps:6400  Discriminator Loss: 0.8901... Generator Loss: 1.5754
Epoch 5/10... steps:6420  Discriminator Loss: 0.9210... Generator Loss: 1.5559
Epoch 5/10... steps:6440  Discriminator Loss: 1.2999... Generator Loss: 0.6785
Epoch 5/10... steps:6460  Discriminator Loss: 1.7326... Generator Loss: 2.7384
Epoch 5/10... steps:6480  Discriminator Loss: 0.6564... Generator Loss: 1.7593
Epoch 5/10... steps:6500  Discriminator Loss: 0.6308... Generator Loss: 2.6165
Epoch 5/10... steps:6520  Discriminator Loss: 1.1811... Generator Loss: 0.7595
Epoch 5/10... steps:6540  Discriminator Loss: 0.9796... Generator Loss: 1.0385
Epoch 5/10... steps:6560  Discriminator Loss: 0.7106... Generator Loss: 1.7815
Epoch 5/10... steps:6580  Discriminator Loss: 0.6795... Generator Loss: 2.1642
Epoch 5/10... steps:6600  Discriminator Loss: 0.8948... Generator Loss: 1.0430
Epoch 5/10... steps:6620  Discriminator Loss: 1.5065... Generator Loss: 0.6137
Epoch 5/10... steps:6640  Discriminator Loss: 0.8255... Generator Loss: 1.2842
Epoch 5/10... steps:6660  Discriminator Loss: 0.7215... Generator Loss: 2.1096
Epoch 5/10... steps:6680  Discriminator Loss: 0.6391... Generator Loss: 1.7748
Epoch 5/10... steps:6700  Discriminator Loss: 0.9537... Generator Loss: 1.0087
Epoch 5/10... steps:6720  Discriminator Loss: 0.6149... Generator Loss: 2.1238
Epoch 5/10... steps:6740  Discriminator Loss: 0.7072... Generator Loss: 1.4997
Epoch 5/10... steps:6760  Discriminator Loss: 0.9991... Generator Loss: 1.0193
Epoch 5/10... steps:6780  Discriminator Loss: 1.1091... Generator Loss: 1.6671
Epoch 5/10... steps:6800  Discriminator Loss: 1.6366... Generator Loss: 2.8171
Epoch 5/10... steps:6820  Discriminator Loss: 1.2760... Generator Loss: 2.3287
Epoch 5/10... steps:6840  Discriminator Loss: 1.3425... Generator Loss: 0.6302
Epoch 5/10... steps:6860  Discriminator Loss: 0.7307... Generator Loss: 3.6681
Epoch 5/10... steps:6880  Discriminator Loss: 0.8538... Generator Loss: 1.1964
Epoch 5/10... steps:6900  Discriminator Loss: 0.6855... Generator Loss: 1.6452
Epoch 5/10... steps:6920  Discriminator Loss: 1.5014... Generator Loss: 0.5056
Epoch 5/10... steps:6940  Discriminator Loss: 0.8945... Generator Loss: 1.4769
Epoch 5/10... steps:6960  Discriminator Loss: 1.7838... Generator Loss: 0.4153
Epoch 5/10... steps:6980  Discriminator Loss: 0.8776... Generator Loss: 1.2179
Epoch 5/10... steps:7000  Discriminator Loss: 0.8165... Generator Loss: 1.2338
Epoch 5/10... steps:7020  Discriminator Loss: 0.7851... Generator Loss: 1.8613
Epoch 5/10... steps:7040  Discriminator Loss: 1.8039... Generator Loss: 3.1222
Epoch 5/10... steps:7060  Discriminator Loss: 1.7315... Generator Loss: 3.1740
Epoch 5/10... steps:7080  Discriminator Loss: 1.0901... Generator Loss: 1.2661
Epoch 5/10... steps:7100  Discriminator Loss: 0.7125... Generator Loss: 2.3052
Epoch 5/10... steps:7120  Discriminator Loss: 0.7052... Generator Loss: 2.1583
Epoch 5/10... steps:7140  Discriminator Loss: 0.7611... Generator Loss: 2.2569
Epoch 5/10... steps:7160  Discriminator Loss: 1.1943... Generator Loss: 1.7224
Epoch 5/10... steps:7180  Discriminator Loss: 1.3566... Generator Loss: 0.5880
Epoch 5/10... steps:7200  Discriminator Loss: 0.6677... Generator Loss: 2.0214
Epoch 5/10... steps:7220  Discriminator Loss: 0.6459... Generator Loss: 2.5461
Epoch 5/10... steps:7240  Discriminator Loss: 0.7808... Generator Loss: 1.4271
Epoch 5/10... steps:7260  Discriminator Loss: 0.6908... Generator Loss: 1.7119
Epoch 5/10... steps:7280  Discriminator Loss: 1.6450... Generator Loss: 3.5613
Epoch 5/10... steps:7300  Discriminator Loss: 1.1295... Generator Loss: 1.0389
Epoch 5/10... steps:7320  Discriminator Loss: 0.8105... Generator Loss: 1.2831
Epoch 5/10... steps:7340  Discriminator Loss: 0.5654... Generator Loss: 2.4179
Epoch 5/10... steps:7360  Discriminator Loss: 0.8150... Generator Loss: 1.2688
Epoch 5/10... steps:7380  Discriminator Loss: 0.5246... Generator Loss: 3.4807
Epoch 5/10... steps:7400  Discriminator Loss: 1.3440... Generator Loss: 0.6317
Epoch 5/10... steps:7420  Discriminator Loss: 1.3912... Generator Loss: 0.5721
Epoch 5/10... steps:7440  Discriminator Loss: 1.5532... Generator Loss: 0.5356
Epoch 5/10... steps:7460  Discriminator Loss: 1.3288... Generator Loss: 0.6764
Epoch 5/10... steps:7480  Discriminator Loss: 0.7408... Generator Loss: 2.2028
Epoch 5/10... steps:7500  Discriminator Loss: 0.8151... Generator Loss: 1.3111
Epoch 5/10... steps:7520  Discriminator Loss: 0.9069... Generator Loss: 1.8714
Epoch 5/10... steps:7540  Discriminator Loss: 0.7354... Generator Loss: 1.8278
Epoch 5/10... steps:7560  Discriminator Loss: 0.9243... Generator Loss: 1.1655
Epoch 5/10... steps:7580  Discriminator Loss: 0.9392... Generator Loss: 3.8668
Epoch 5/10... steps:7600  Discriminator Loss: 1.0877... Generator Loss: 1.1185
Epoch 5/10... steps:7620  Discriminator Loss: 0.9035... Generator Loss: 1.1187
Epoch 5/10... steps:7640  Discriminator Loss: 0.8354... Generator Loss: 1.9654
Epoch 5/10... steps:7660  Discriminator Loss: 0.8258... Generator Loss: 1.2111
Epoch 5/10... steps:7680  Discriminator Loss: 1.0136... Generator Loss: 1.6552
Epoch 5/10... steps:7700  Discriminator Loss: 0.8117... Generator Loss: 1.8102
Epoch 5/10... steps:7720  Discriminator Loss: 1.1450... Generator Loss: 0.8065
Epoch 5/10... steps:7740  Discriminator Loss: 0.7839... Generator Loss: 1.5721
Epoch 5/10... steps:7760  Discriminator Loss: 0.6755... Generator Loss: 1.6828
Epoch 5/10... steps:7780  Discriminator Loss: 0.5851... Generator Loss: 2.6427
Epoch 5/10... steps:7800  Discriminator Loss: 2.1493... Generator Loss: 4.8161
Epoch 5/10... steps:7820  Discriminator Loss: 1.5327... Generator Loss: 3.0697
Epoch 5/10... steps:7840  Discriminator Loss: 0.6964... Generator Loss: 1.5476
Epoch 5/10... steps:7860  Discriminator Loss: 0.6599... Generator Loss: 1.8990
Epoch 5/10... steps:7880  Discriminator Loss: 1.3314... Generator Loss: 0.7432
Epoch 5/10... steps:7900  Discriminator Loss: 0.5789... Generator Loss: 2.4814
Epoch 6/10... steps:7920  Discriminator Loss: 0.8440... Generator Loss: 1.2676
Epoch 6/10... steps:7940  Discriminator Loss: 1.6419... Generator Loss: 0.4619
Epoch 6/10... steps:7960  Discriminator Loss: 0.8744... Generator Loss: 1.1570
Epoch 6/10... steps:7980  Discriminator Loss: 0.8969... Generator Loss: 1.6748
Epoch 6/10... steps:8000  Discriminator Loss: 0.6211... Generator Loss: 1.8767
Epoch 6/10... steps:8020  Discriminator Loss: 0.6534... Generator Loss: 1.7818
Epoch 6/10... steps:8040  Discriminator Loss: 0.7263... Generator Loss: 1.8198
Epoch 6/10... steps:8060  Discriminator Loss: 0.6351... Generator Loss: 2.6121
Epoch 6/10... steps:8080  Discriminator Loss: 0.9387... Generator Loss: 1.3107
Epoch 6/10... steps:8100  Discriminator Loss: 1.8990... Generator Loss: 0.3251
Epoch 6/10... steps:8120  Discriminator Loss: 1.4535... Generator Loss: 0.6036
Epoch 6/10... steps:8140  Discriminator Loss: 0.7700... Generator Loss: 1.3747
Epoch 6/10... steps:8160  Discriminator Loss: 1.4695... Generator Loss: 1.0052
Epoch 6/10... steps:8180  Discriminator Loss: 0.7631... Generator Loss: 1.8126
Epoch 6/10... steps:8200  Discriminator Loss: 2.0154... Generator Loss: 0.3394
Epoch 6/10... steps:8220  Discriminator Loss: 0.9252... Generator Loss: 2.3800
Epoch 6/10... steps:8240  Discriminator Loss: 0.7502... Generator Loss: 1.8781
Epoch 6/10... steps:8260  Discriminator Loss: 1.3292... Generator Loss: 3.1377
Epoch 6/10... steps:8280  Discriminator Loss: 0.7596... Generator Loss: 1.3919
Epoch 6/10... steps:8300  Discriminator Loss: 0.5997... Generator Loss: 2.2008
Epoch 6/10... steps:8320  Discriminator Loss: 1.0167... Generator Loss: 0.9754
Epoch 6/10... steps:8340  Discriminator Loss: 1.9921... Generator Loss: 0.3503
Epoch 6/10... steps:8360  Discriminator Loss: 0.6467... Generator Loss: 2.1218
Epoch 6/10... steps:8380  Discriminator Loss: 0.8630... Generator Loss: 1.2339
Epoch 6/10... steps:8400  Discriminator Loss: 0.6617... Generator Loss: 2.9209
Epoch 6/10... steps:8420  Discriminator Loss: 1.1775... Generator Loss: 0.7506
Epoch 6/10... steps:8440  Discriminator Loss: 1.2343... Generator Loss: 0.8082
Epoch 6/10... steps:8460  Discriminator Loss: 0.7685... Generator Loss: 1.3382
Epoch 6/10... steps:8480  Discriminator Loss: 0.7241... Generator Loss: 1.5182
Epoch 6/10... steps:8500  Discriminator Loss: 1.7344... Generator Loss: 0.3996
Epoch 6/10... steps:8520  Discriminator Loss: 0.7557... Generator Loss: 1.3930
Epoch 6/10... steps:8540  Discriminator Loss: 2.4055... Generator Loss: 4.5295
Epoch 6/10... steps:8560  Discriminator Loss: 1.2551... Generator Loss: 1.6809
Epoch 6/10... steps:8580  Discriminator Loss: 0.7619... Generator Loss: 1.4526
Epoch 6/10... steps:8600  Discriminator Loss: 0.8915... Generator Loss: 1.0950
Epoch 6/10... steps:8620  Discriminator Loss: 1.9912... Generator Loss: 3.2898
Epoch 6/10... steps:8640  Discriminator Loss: 0.7748... Generator Loss: 1.6170
Epoch 6/10... steps:8660  Discriminator Loss: 0.6821... Generator Loss: 1.6212
Epoch 6/10... steps:8680  Discriminator Loss: 0.9054... Generator Loss: 1.1973
Epoch 6/10... steps:8700  Discriminator Loss: 0.6271... Generator Loss: 1.9576
Epoch 6/10... steps:8720  Discriminator Loss: 0.7024... Generator Loss: 1.8573
Epoch 6/10... steps:8740  Discriminator Loss: 1.1867... Generator Loss: 2.7994
Epoch 6/10... steps:8760  Discriminator Loss: 0.9080... Generator Loss: 1.2168
Epoch 6/10... steps:8780  Discriminator Loss: 1.0123... Generator Loss: 1.8395
Epoch 6/10... steps:8800  Discriminator Loss: 1.1312... Generator Loss: 1.6894
Epoch 6/10... steps:8820  Discriminator Loss: 0.7187... Generator Loss: 1.6988
Epoch 6/10... steps:8840  Discriminator Loss: 1.2117... Generator Loss: 0.7227
Epoch 6/10... steps:8860  Discriminator Loss: 1.0513... Generator Loss: 3.0988
Epoch 6/10... steps:8880  Discriminator Loss: 0.6191... Generator Loss: 2.2268
Epoch 6/10... steps:8900  Discriminator Loss: 0.7036... Generator Loss: 1.8652
Epoch 6/10... steps:8920  Discriminator Loss: 1.0880... Generator Loss: 3.7301
Epoch 6/10... steps:8940  Discriminator Loss: 1.2963... Generator Loss: 2.6312
Epoch 6/10... steps:8960  Discriminator Loss: 0.6891... Generator Loss: 1.5703
Epoch 6/10... steps:8980  Discriminator Loss: 1.3610... Generator Loss: 0.6381
Epoch 6/10... steps:9000  Discriminator Loss: 1.0101... Generator Loss: 0.9824
Epoch 6/10... steps:9020  Discriminator Loss: 0.8523... Generator Loss: 1.2051
Epoch 6/10... steps:9040  Discriminator Loss: 1.3857... Generator Loss: 3.0403
Epoch 6/10... steps:9060  Discriminator Loss: 1.0641... Generator Loss: 0.9504
Epoch 6/10... steps:9080  Discriminator Loss: 0.8477... Generator Loss: 1.1554
Epoch 6/10... steps:9100  Discriminator Loss: 1.1555... Generator Loss: 3.2872
Epoch 6/10... steps:9120  Discriminator Loss: 0.8556... Generator Loss: 1.1730
Epoch 6/10... steps:9140  Discriminator Loss: 1.2325... Generator Loss: 0.7804
Epoch 6/10... steps:9160  Discriminator Loss: 1.7376... Generator Loss: 0.4280
Epoch 6/10... steps:9180  Discriminator Loss: 0.5906... Generator Loss: 2.4621
Epoch 6/10... steps:9200  Discriminator Loss: 0.6404... Generator Loss: 2.4693
Epoch 6/10... steps:9220  Discriminator Loss: 0.8096... Generator Loss: 2.0022
Epoch 6/10... steps:9240  Discriminator Loss: 0.6655... Generator Loss: 1.6181
Epoch 6/10... steps:9260  Discriminator Loss: 0.6633... Generator Loss: 1.7147
Epoch 6/10... steps:9280  Discriminator Loss: 1.8740... Generator Loss: 2.8202
Epoch 6/10... steps:9300  Discriminator Loss: 1.8248... Generator Loss: 1.6161
Epoch 6/10... steps:9320  Discriminator Loss: 1.7253... Generator Loss: 0.4510
Epoch 6/10... steps:9340  Discriminator Loss: 0.8000... Generator Loss: 1.3719
Epoch 6/10... steps:9360  Discriminator Loss: 0.8765... Generator Loss: 1.3079
Epoch 6/10... steps:9380  Discriminator Loss: 1.3094... Generator Loss: 0.6896
Epoch 6/10... steps:9400  Discriminator Loss: 0.7231... Generator Loss: 1.4429
Epoch 6/10... steps:9420  Discriminator Loss: 0.6548... Generator Loss: 1.8973
Epoch 6/10... steps:9440  Discriminator Loss: 1.2385... Generator Loss: 2.2000
Epoch 6/10... steps:9460  Discriminator Loss: 1.3505... Generator Loss: 0.6777
Epoch 6/10... steps:9480  Discriminator Loss: 0.7555... Generator Loss: 3.5457
Epoch 7/10... steps:9500  Discriminator Loss: 0.6619... Generator Loss: 1.6190
Epoch 7/10... steps:9520  Discriminator Loss: 0.7842... Generator Loss: 1.2311
Epoch 7/10... steps:9540  Discriminator Loss: 1.1977... Generator Loss: 0.7332
Epoch 7/10... steps:9560  Discriminator Loss: 0.6387... Generator Loss: 1.7696
Epoch 7/10... steps:9580  Discriminator Loss: 0.5361... Generator Loss: 2.4514
Epoch 7/10... steps:9600  Discriminator Loss: 0.6210... Generator Loss: 1.9365
Epoch 7/10... steps:9620  Discriminator Loss: 0.5535... Generator Loss: 3.4435
Epoch 7/10... steps:9640  Discriminator Loss: 1.1016... Generator Loss: 1.7630
Epoch 7/10... steps:9660  Discriminator Loss: 0.5948... Generator Loss: 2.1864
Epoch 7/10... steps:9680  Discriminator Loss: 1.3674... Generator Loss: 0.7361
Epoch 7/10... steps:9700  Discriminator Loss: 0.9623... Generator Loss: 1.2823
Epoch 7/10... steps:9720  Discriminator Loss: 1.3737... Generator Loss: 0.6324
Epoch 7/10... steps:9740  Discriminator Loss: 0.5854... Generator Loss: 2.1884
Epoch 7/10... steps:9760  Discriminator Loss: 0.8235... Generator Loss: 1.3784
Epoch 7/10... steps:9780  Discriminator Loss: 0.9524... Generator Loss: 1.0071
Epoch 7/10... steps:9800  Discriminator Loss: 1.0254... Generator Loss: 1.0257
Epoch 7/10... steps:9820  Discriminator Loss: 0.7758... Generator Loss: 1.3964
Epoch 7/10... steps:9840  Discriminator Loss: 0.9705... Generator Loss: 1.0127
Epoch 7/10... steps:9860  Discriminator Loss: 0.7673... Generator Loss: 1.4786
Epoch 7/10... steps:9880  Discriminator Loss: 1.2180... Generator Loss: 2.0452
Epoch 7/10... steps:9900  Discriminator Loss: 2.5030... Generator Loss: 2.7605
Epoch 7/10... steps:9920  Discriminator Loss: 1.2730... Generator Loss: 0.7774
Epoch 7/10... steps:9940  Discriminator Loss: 1.1656... Generator Loss: 0.8035
Epoch 7/10... steps:9960  Discriminator Loss: 0.7623... Generator Loss: 1.5460
Epoch 7/10... steps:9980  Discriminator Loss: 0.7251... Generator Loss: 1.7514
Epoch 7/10... steps:10000  Discriminator Loss: 1.0615... Generator Loss: 0.8690
Epoch 7/10... steps:10020  Discriminator Loss: 1.3052... Generator Loss: 0.6644
Epoch 7/10... steps:10040  Discriminator Loss: 0.7978... Generator Loss: 1.4253
Epoch 7/10... steps:10060  Discriminator Loss: 0.5840... Generator Loss: 2.0779
Epoch 7/10... steps:10080  Discriminator Loss: 1.4817... Generator Loss: 0.5998
Epoch 7/10... steps:10100  Discriminator Loss: 1.0823... Generator Loss: 0.8630
Epoch 7/10... steps:10120  Discriminator Loss: 0.7201... Generator Loss: 1.4961
Epoch 7/10... steps:10140  Discriminator Loss: 0.8530... Generator Loss: 3.2684
Epoch 7/10... steps:10160  Discriminator Loss: 1.2550... Generator Loss: 0.6996
Epoch 7/10... steps:10180  Discriminator Loss: 0.7025... Generator Loss: 2.6306
Epoch 7/10... steps:10200  Discriminator Loss: 0.7964... Generator Loss: 1.3512
Epoch 7/10... steps:10220  Discriminator Loss: 0.7663... Generator Loss: 1.5039
Epoch 7/10... steps:10240  Discriminator Loss: 1.0384... Generator Loss: 0.9235
Epoch 7/10... steps:10260  Discriminator Loss: 1.2140... Generator Loss: 1.0086
Epoch 7/10... steps:10280  Discriminator Loss: 0.8058... Generator Loss: 1.3531
Epoch 7/10... steps:10300  Discriminator Loss: 0.8598... Generator Loss: 2.1541
Epoch 7/10... steps:10320  Discriminator Loss: 0.5581... Generator Loss: 2.2920
Epoch 7/10... steps:10340  Discriminator Loss: 1.0820... Generator Loss: 0.9030
Epoch 7/10... steps:10360  Discriminator Loss: 0.9675... Generator Loss: 1.0690
Epoch 7/10... steps:10380  Discriminator Loss: 0.7537... Generator Loss: 2.8580
Epoch 7/10... steps:10400  Discriminator Loss: 0.6228... Generator Loss: 1.8540
Epoch 7/10... steps:10420  Discriminator Loss: 0.8964... Generator Loss: 1.0842
Epoch 7/10... steps:10440  Discriminator Loss: 0.7542... Generator Loss: 1.8384
Epoch 7/10... steps:10460  Discriminator Loss: 0.8441... Generator Loss: 1.3456
Epoch 7/10... steps:10480  Discriminator Loss: 0.6768... Generator Loss: 1.9345
Epoch 7/10... steps:10500  Discriminator Loss: 0.9887... Generator Loss: 0.9725
Epoch 7/10... steps:10520  Discriminator Loss: 0.8737... Generator Loss: 1.1470
Epoch 7/10... steps:10540  Discriminator Loss: 1.7252... Generator Loss: 0.5907
Epoch 7/10... steps:10560  Discriminator Loss: 1.4009... Generator Loss: 0.5914
Epoch 7/10... steps:10580  Discriminator Loss: 1.2208... Generator Loss: 0.7008
Epoch 7/10... steps:10600  Discriminator Loss: 0.6306... Generator Loss: 1.8263
Epoch 7/10... steps:10620  Discriminator Loss: 0.8440... Generator Loss: 2.4474
Epoch 7/10... steps:10640  Discriminator Loss: 1.0120... Generator Loss: 1.0266
Epoch 7/10... steps:10660  Discriminator Loss: 0.7243... Generator Loss: 2.0454
Epoch 7/10... steps:10680  Discriminator Loss: 1.1769... Generator Loss: 1.3537
Epoch 7/10... steps:10700  Discriminator Loss: 1.1258... Generator Loss: 0.8149
Epoch 7/10... steps:10720  Discriminator Loss: 1.0394... Generator Loss: 0.9213
Epoch 7/10... steps:10740  Discriminator Loss: 0.5934... Generator Loss: 2.1197
Epoch 7/10... steps:10760  Discriminator Loss: 0.9663... Generator Loss: 1.0150
Epoch 7/10... steps:10780  Discriminator Loss: 1.7361... Generator Loss: 2.7042
Epoch 7/10... steps:10800  Discriminator Loss: 1.1878... Generator Loss: 0.8621
Epoch 7/10... steps:10820  Discriminator Loss: 0.6845... Generator Loss: 1.8685
Epoch 7/10... steps:10840  Discriminator Loss: 0.9003... Generator Loss: 2.0355
Epoch 7/10... steps:10860  Discriminator Loss: 0.6093... Generator Loss: 2.2022
Epoch 7/10... steps:10880  Discriminator Loss: 1.5102... Generator Loss: 0.5845
Epoch 7/10... steps:10900  Discriminator Loss: 1.5625... Generator Loss: 0.5706
Epoch 7/10... steps:10920  Discriminator Loss: 1.5786... Generator Loss: 3.3724
Epoch 7/10... steps:10940  Discriminator Loss: 0.8739... Generator Loss: 1.3696
Epoch 7/10... steps:10960  Discriminator Loss: 0.8752... Generator Loss: 1.2740
Epoch 7/10... steps:10980  Discriminator Loss: 0.5654... Generator Loss: 2.3661
Epoch 7/10... steps:11000  Discriminator Loss: 1.1468... Generator Loss: 1.1291
Epoch 7/10... steps:11020  Discriminator Loss: 0.6224... Generator Loss: 2.1499
Epoch 7/10... steps:11040  Discriminator Loss: 1.4570... Generator Loss: 0.5991
Epoch 7/10... steps:11060  Discriminator Loss: 0.8585... Generator Loss: 2.2574
Epoch 8/10... steps:11080  Discriminator Loss: 0.7423... Generator Loss: 2.0726
Epoch 8/10... steps:11100  Discriminator Loss: 0.9308... Generator Loss: 1.0443
Epoch 8/10... steps:11120  Discriminator Loss: 0.5886... Generator Loss: 1.9917
Epoch 8/10... steps:11140  Discriminator Loss: 0.6963... Generator Loss: 1.7996
Epoch 8/10... steps:11160  Discriminator Loss: 0.7945... Generator Loss: 2.5923
Epoch 8/10... steps:11180  Discriminator Loss: 0.6041... Generator Loss: 2.5363
Epoch 8/10... steps:11200  Discriminator Loss: 0.7380... Generator Loss: 1.4609
Epoch 8/10... steps:11220  Discriminator Loss: 1.4227... Generator Loss: 4.4150
Epoch 8/10... steps:11240  Discriminator Loss: 0.7479... Generator Loss: 1.4878
Epoch 8/10... steps:11260  Discriminator Loss: 0.7134... Generator Loss: 1.5338
Epoch 8/10... steps:11280  Discriminator Loss: 0.8340... Generator Loss: 1.8036
Epoch 8/10... steps:11300  Discriminator Loss: 1.0665... Generator Loss: 0.8493
Epoch 8/10... steps:11320  Discriminator Loss: 1.1872... Generator Loss: 0.8154
Epoch 8/10... steps:11340  Discriminator Loss: 0.7485... Generator Loss: 1.4901
Epoch 8/10... steps:11360  Discriminator Loss: 0.7811... Generator Loss: 1.3815
Epoch 8/10... steps:11380  Discriminator Loss: 2.1964... Generator Loss: 0.2637
Epoch 8/10... steps:11400  Discriminator Loss: 0.7586... Generator Loss: 2.4392
Epoch 8/10... steps:11420  Discriminator Loss: 1.4115... Generator Loss: 2.6770
Epoch 8/10... steps:11440  Discriminator Loss: 0.8976... Generator Loss: 1.9291
Epoch 8/10... steps:11460  Discriminator Loss: 0.8845... Generator Loss: 1.1098
Epoch 8/10... steps:11480  Discriminator Loss: 0.6746... Generator Loss: 1.8595
Epoch 8/10... steps:11500  Discriminator Loss: 0.6730... Generator Loss: 1.6973
Epoch 8/10... steps:11520  Discriminator Loss: 1.0820... Generator Loss: 1.6272
Epoch 8/10... steps:11540  Discriminator Loss: 0.7476... Generator Loss: 1.3833
Epoch 8/10... steps:11560  Discriminator Loss: 0.9663... Generator Loss: 1.5450
Epoch 8/10... steps:11580  Discriminator Loss: 0.7987... Generator Loss: 1.7927
Epoch 8/10... steps:11600  Discriminator Loss: 0.8577... Generator Loss: 1.1440
Epoch 8/10... steps:11620  Discriminator Loss: 0.6851... Generator Loss: 1.6448
Epoch 8/10... steps:11640  Discriminator Loss: 0.6326... Generator Loss: 1.7791
Epoch 8/10... steps:11660  Discriminator Loss: 1.1889... Generator Loss: 2.0893
Epoch 8/10... steps:11680  Discriminator Loss: 0.8669... Generator Loss: 1.1502
Epoch 8/10... steps:11700  Discriminator Loss: 1.5728... Generator Loss: 0.5277
Epoch 8/10... steps:11720  Discriminator Loss: 0.7067... Generator Loss: 1.7104
Epoch 8/10... steps:11740  Discriminator Loss: 1.0388... Generator Loss: 0.8677
Epoch 8/10... steps:11760  Discriminator Loss: 0.7669... Generator Loss: 1.5294
Epoch 8/10... steps:11780  Discriminator Loss: 1.4336... Generator Loss: 0.6735
Epoch 8/10... steps:11800  Discriminator Loss: 0.6341... Generator Loss: 2.4105
Epoch 8/10... steps:11820  Discriminator Loss: 0.6884... Generator Loss: 1.6669
Epoch 8/10... steps:11840  Discriminator Loss: 1.9111... Generator Loss: 2.7993
Epoch 8/10... steps:11860  Discriminator Loss: 0.8323... Generator Loss: 1.3732
Epoch 8/10... steps:11880  Discriminator Loss: 1.1621... Generator Loss: 0.8863
Epoch 8/10... steps:11900  Discriminator Loss: 0.8800... Generator Loss: 2.1895
Epoch 8/10... steps:11920  Discriminator Loss: 0.9080... Generator Loss: 2.0212
Epoch 8/10... steps:11940  Discriminator Loss: 0.9237... Generator Loss: 1.1057
Epoch 8/10... steps:11960  Discriminator Loss: 0.7002... Generator Loss: 2.5131
Epoch 8/10... steps:11980  Discriminator Loss: 0.8094... Generator Loss: 1.2541
Epoch 8/10... steps:12000  Discriminator Loss: 1.7015... Generator Loss: 0.5018
Epoch 8/10... steps:12020  Discriminator Loss: 0.7747... Generator Loss: 2.2412
Epoch 8/10... steps:12040  Discriminator Loss: 0.8055... Generator Loss: 1.5772
Epoch 8/10... steps:12060  Discriminator Loss: 0.7276... Generator Loss: 3.3991
Epoch 8/10... steps:12080  Discriminator Loss: 0.8095... Generator Loss: 2.7495
Epoch 8/10... steps:12100  Discriminator Loss: 2.1466... Generator Loss: 3.9600
Epoch 8/10... steps:12120  Discriminator Loss: 1.8388... Generator Loss: 0.4301
Epoch 8/10... steps:12140  Discriminator Loss: 0.8486... Generator Loss: 4.1248
Epoch 8/10... steps:12160  Discriminator Loss: 1.0113... Generator Loss: 0.9915
Epoch 8/10... steps:12180  Discriminator Loss: 0.6954... Generator Loss: 1.6012
Epoch 8/10... steps:12200  Discriminator Loss: 0.7618... Generator Loss: 1.3317
Epoch 8/10... steps:12220  Discriminator Loss: 0.5414... Generator Loss: 2.7794
Epoch 8/10... steps:12240  Discriminator Loss: 0.6909... Generator Loss: 1.7079
Epoch 8/10... steps:12260  Discriminator Loss: 0.5568... Generator Loss: 2.9104
Epoch 8/10... steps:12280  Discriminator Loss: 0.6670... Generator Loss: 1.7804
Epoch 8/10... steps:12300  Discriminator Loss: 0.6990... Generator Loss: 1.5683
Epoch 8/10... steps:12320  Discriminator Loss: 1.4406... Generator Loss: 0.7300
Epoch 8/10... steps:12340  Discriminator Loss: 2.0877... Generator Loss: 3.8221
Epoch 8/10... steps:12360  Discriminator Loss: 1.3037... Generator Loss: 0.6432
Epoch 8/10... steps:12380  Discriminator Loss: 0.7573... Generator Loss: 1.3387
Epoch 8/10... steps:12400  Discriminator Loss: 0.8369... Generator Loss: 1.7216
Epoch 8/10... steps:12420  Discriminator Loss: 0.7053... Generator Loss: 1.8438
Epoch 8/10... steps:12440  Discriminator Loss: 1.4573... Generator Loss: 2.6632
Epoch 8/10... steps:12460  Discriminator Loss: 2.7748... Generator Loss: 0.2514
Epoch 8/10... steps:12480  Discriminator Loss: 0.9777... Generator Loss: 2.0639
Epoch 8/10... steps:12500  Discriminator Loss: 0.8609... Generator Loss: 1.1800
Epoch 8/10... steps:12520  Discriminator Loss: 1.2550... Generator Loss: 0.7126
Epoch 8/10... steps:12540  Discriminator Loss: 1.4415... Generator Loss: 0.5619
Epoch 8/10... steps:12560  Discriminator Loss: 0.8143... Generator Loss: 1.4147
Epoch 8/10... steps:12580  Discriminator Loss: 1.2753... Generator Loss: 0.7784
Epoch 8/10... steps:12600  Discriminator Loss: 1.2658... Generator Loss: 0.7570
Epoch 8/10... steps:12620  Discriminator Loss: 1.9188... Generator Loss: 0.3869
Epoch 8/10... steps:12640  Discriminator Loss: 0.7464... Generator Loss: 1.6951
Epoch 9/10... steps:12660  Discriminator Loss: 0.7680... Generator Loss: 1.3286
Epoch 9/10... steps:12680  Discriminator Loss: 0.6355... Generator Loss: 2.0904
Epoch 9/10... steps:12700  Discriminator Loss: 0.7386... Generator Loss: 1.5954
Epoch 9/10... steps:12720  Discriminator Loss: 0.8848... Generator Loss: 2.8408
Epoch 9/10... steps:12740  Discriminator Loss: 0.6860... Generator Loss: 2.2512
Epoch 9/10... steps:12760  Discriminator Loss: 0.6403... Generator Loss: 2.1891
Epoch 9/10... steps:12780  Discriminator Loss: 1.7115... Generator Loss: 0.7610
Epoch 9/10... steps:12800  Discriminator Loss: 1.1793... Generator Loss: 1.1358
Epoch 9/10... steps:12820  Discriminator Loss: 0.7807... Generator Loss: 2.3235
Epoch 9/10... steps:12840  Discriminator Loss: 0.8019... Generator Loss: 1.2912
Epoch 9/10... steps:12860  Discriminator Loss: 0.7656... Generator Loss: 1.5005
Epoch 9/10... steps:12880  Discriminator Loss: 0.6975... Generator Loss: 2.5018
Epoch 9/10... steps:12900  Discriminator Loss: 1.0700... Generator Loss: 0.9461
Epoch 9/10... steps:12920  Discriminator Loss: 1.1833... Generator Loss: 2.9113
Epoch 9/10... steps:12940  Discriminator Loss: 1.5796... Generator Loss: 0.4981
Epoch 9/10... steps:12960  Discriminator Loss: 0.6786... Generator Loss: 2.0614
Epoch 9/10... steps:12980  Discriminator Loss: 0.6352... Generator Loss: 2.2741
Epoch 9/10... steps:13000  Discriminator Loss: 1.0898... Generator Loss: 0.9277
Epoch 9/10... steps:13020  Discriminator Loss: 0.8902... Generator Loss: 1.2277
Epoch 9/10... steps:13040  Discriminator Loss: 0.8508... Generator Loss: 1.5824
Epoch 9/10... steps:13060  Discriminator Loss: 0.7659... Generator Loss: 1.3981
Epoch 9/10... steps:13080  Discriminator Loss: 0.8654... Generator Loss: 1.2193
Epoch 9/10... steps:13100  Discriminator Loss: 0.8676... Generator Loss: 1.1803
Epoch 9/10... steps:13120  Discriminator Loss: 0.6788... Generator Loss: 2.4186
Epoch 9/10... steps:13140  Discriminator Loss: 0.7457... Generator Loss: 1.4361
Epoch 9/10... steps:13160  Discriminator Loss: 0.8095... Generator Loss: 1.4219
Epoch 9/10... steps:13180  Discriminator Loss: 0.7493... Generator Loss: 1.3543
Epoch 9/10... steps:13200  Discriminator Loss: 0.5483... Generator Loss: 2.1063
Epoch 9/10... steps:13220  Discriminator Loss: 0.5637... Generator Loss: 2.1704
Epoch 9/10... steps:13240  Discriminator Loss: 0.5831... Generator Loss: 2.2435
Epoch 9/10... steps:13260  Discriminator Loss: 1.0952... Generator Loss: 1.8928
Epoch 9/10... steps:13280  Discriminator Loss: 1.5801... Generator Loss: 0.5329
Epoch 9/10... steps:13300  Discriminator Loss: 2.3799... Generator Loss: 0.2524
Epoch 9/10... steps:13320  Discriminator Loss: 1.0803... Generator Loss: 0.9145
Epoch 9/10... steps:13340  Discriminator Loss: 0.8717... Generator Loss: 1.4974
Epoch 9/10... steps:13360  Discriminator Loss: 0.8957... Generator Loss: 3.2665
Epoch 9/10... steps:13380  Discriminator Loss: 1.0454... Generator Loss: 0.8868
Epoch 9/10... steps:13400  Discriminator Loss: 0.9783... Generator Loss: 0.9990
Epoch 9/10... steps:13420  Discriminator Loss: 0.9485... Generator Loss: 1.3447
Epoch 9/10... steps:13440  Discriminator Loss: 1.8607... Generator Loss: 0.3850
Epoch 9/10... steps:13460  Discriminator Loss: 0.5348... Generator Loss: 2.5746
Epoch 9/10... steps:13480  Discriminator Loss: 0.7209... Generator Loss: 1.5489
Epoch 9/10... steps:13500  Discriminator Loss: 1.9681... Generator Loss: 0.8631
Epoch 9/10... steps:13520  Discriminator Loss: 1.2833... Generator Loss: 0.7081
Epoch 9/10... steps:13540  Discriminator Loss: 0.8243... Generator Loss: 1.7036
Epoch 9/10... steps:13560  Discriminator Loss: 0.9329... Generator Loss: 1.1773
Epoch 9/10... steps:13580  Discriminator Loss: 0.8726... Generator Loss: 1.2296
Epoch 9/10... steps:13600  Discriminator Loss: 0.6148... Generator Loss: 1.9242
Epoch 9/10... steps:13620  Discriminator Loss: 1.3090... Generator Loss: 2.1199
Epoch 9/10... steps:13640  Discriminator Loss: 0.8850... Generator Loss: 1.1805
Epoch 9/10... steps:13660  Discriminator Loss: 0.7768... Generator Loss: 1.5090
Epoch 9/10... steps:13680  Discriminator Loss: 0.8295... Generator Loss: 1.6668
Epoch 9/10... steps:13700  Discriminator Loss: 0.8740... Generator Loss: 1.2442
Epoch 9/10... steps:13720  Discriminator Loss: 0.6025... Generator Loss: 2.5151
Epoch 9/10... steps:13740  Discriminator Loss: 0.7599... Generator Loss: 2.3582
Epoch 9/10... steps:13760  Discriminator Loss: 0.5028... Generator Loss: 2.4783
Epoch 9/10... steps:13780  Discriminator Loss: 0.6497... Generator Loss: 1.8576
Epoch 9/10... steps:13800  Discriminator Loss: 0.7765... Generator Loss: 1.6102
Epoch 9/10... steps:13820  Discriminator Loss: 0.6990... Generator Loss: 1.6746
Epoch 9/10... steps:13840  Discriminator Loss: 0.5211... Generator Loss: 2.4624
Epoch 9/10... steps:13860  Discriminator Loss: 0.7640... Generator Loss: 1.5813
Epoch 9/10... steps:13880  Discriminator Loss: 0.6821... Generator Loss: 1.6892
Epoch 9/10... steps:13900  Discriminator Loss: 1.9552... Generator Loss: 2.1071
Epoch 9/10... steps:13920  Discriminator Loss: 1.2873... Generator Loss: 2.3752
Epoch 9/10... steps:13940  Discriminator Loss: 0.6750... Generator Loss: 2.1676
Epoch 9/10... steps:13960  Discriminator Loss: 0.9720... Generator Loss: 2.7333
Epoch 9/10... steps:13980  Discriminator Loss: 1.1658... Generator Loss: 0.7893
Epoch 9/10... steps:14000  Discriminator Loss: 0.8992... Generator Loss: 1.1104
Epoch 9/10... steps:14020  Discriminator Loss: 0.7871... Generator Loss: 1.4072
Epoch 9/10... steps:14040  Discriminator Loss: 0.9488... Generator Loss: 1.0637
Epoch 9/10... steps:14060  Discriminator Loss: 1.1092... Generator Loss: 0.8908
Epoch 9/10... steps:14080  Discriminator Loss: 0.9712... Generator Loss: 0.9880
Epoch 9/10... steps:14100  Discriminator Loss: 0.7433... Generator Loss: 1.4231
Epoch 9/10... steps:14120  Discriminator Loss: 0.7381... Generator Loss: 1.4318
Epoch 9/10... steps:14140  Discriminator Loss: 0.5541... Generator Loss: 2.3286
Epoch 9/10... steps:14160  Discriminator Loss: 0.7419... Generator Loss: 1.8325
Epoch 9/10... steps:14180  Discriminator Loss: 0.7889... Generator Loss: 1.3819
Epoch 9/10... steps:14200  Discriminator Loss: 1.2104... Generator Loss: 0.7263
Epoch 9/10... steps:14220  Discriminator Loss: 0.6416... Generator Loss: 2.2388
Epoch 10/10... steps:14240  Discriminator Loss: 0.9479... Generator Loss: 1.0514
Epoch 10/10... steps:14260  Discriminator Loss: 0.6598... Generator Loss: 1.8230
Epoch 10/10... steps:14280  Discriminator Loss: 0.8481... Generator Loss: 2.0865
Epoch 10/10... steps:14300  Discriminator Loss: 0.7039... Generator Loss: 1.8522
Epoch 10/10... steps:14320  Discriminator Loss: 1.0250... Generator Loss: 1.0719
Epoch 10/10... steps:14340  Discriminator Loss: 0.9896... Generator Loss: 0.9954
Epoch 10/10... steps:14360  Discriminator Loss: 1.6856... Generator Loss: 2.2141
Epoch 10/10... steps:14380  Discriminator Loss: 1.3708... Generator Loss: 0.6865
Epoch 10/10... steps:14400  Discriminator Loss: 0.8635... Generator Loss: 2.8809
Epoch 10/10... steps:14420  Discriminator Loss: 1.0127... Generator Loss: 0.9962
Epoch 10/10... steps:14440  Discriminator Loss: 0.7418... Generator Loss: 1.7357
Epoch 10/10... steps:14460  Discriminator Loss: 0.5442... Generator Loss: 2.5293
Epoch 10/10... steps:14480  Discriminator Loss: 0.8434... Generator Loss: 2.9841
Epoch 10/10... steps:14500  Discriminator Loss: 0.6404... Generator Loss: 1.8704
Epoch 10/10... steps:14520  Discriminator Loss: 0.6204... Generator Loss: 2.1169
Epoch 10/10... steps:14540  Discriminator Loss: 1.0044... Generator Loss: 2.3638
Epoch 10/10... steps:14560  Discriminator Loss: 0.8575... Generator Loss: 1.2248
Epoch 10/10... steps:14580  Discriminator Loss: 1.6020... Generator Loss: 0.4834
Epoch 10/10... steps:14600  Discriminator Loss: 0.8774... Generator Loss: 2.3651
Epoch 10/10... steps:14620  Discriminator Loss: 0.7821... Generator Loss: 2.8863
Epoch 10/10... steps:14640  Discriminator Loss: 0.9011... Generator Loss: 2.2196
Epoch 10/10... steps:14660  Discriminator Loss: 0.8769... Generator Loss: 2.5285
Epoch 10/10... steps:14680  Discriminator Loss: 0.6739... Generator Loss: 1.8022
Epoch 10/10... steps:14700  Discriminator Loss: 0.6038... Generator Loss: 1.9881
Epoch 10/10... steps:14720  Discriminator Loss: 1.4201... Generator Loss: 3.2473
Epoch 10/10... steps:14740  Discriminator Loss: 0.7034... Generator Loss: 2.5416
Epoch 10/10... steps:14760  Discriminator Loss: 1.4868... Generator Loss: 0.6054
Epoch 10/10... steps:14780  Discriminator Loss: 0.7277... Generator Loss: 1.4743
Epoch 10/10... steps:14800  Discriminator Loss: 0.6664... Generator Loss: 1.7749
Epoch 10/10... steps:14820  Discriminator Loss: 0.9307... Generator Loss: 2.2194
Epoch 10/10... steps:14840  Discriminator Loss: 0.8735... Generator Loss: 1.1939
Epoch 10/10... steps:14860  Discriminator Loss: 1.5487... Generator Loss: 0.6056
Epoch 10/10... steps:14880  Discriminator Loss: 1.5641... Generator Loss: 0.5104
Epoch 10/10... steps:14900  Discriminator Loss: 0.7781... Generator Loss: 1.6143
Epoch 10/10... steps:14920  Discriminator Loss: 0.6262... Generator Loss: 1.9055
Epoch 10/10... steps:14940  Discriminator Loss: 1.2054... Generator Loss: 1.3057
Epoch 10/10... steps:14960  Discriminator Loss: 1.2988... Generator Loss: 0.8871
Epoch 10/10... steps:14980  Discriminator Loss: 0.8518... Generator Loss: 2.1406
Epoch 10/10... steps:15000  Discriminator Loss: 2.0054... Generator Loss: 3.6269
Epoch 10/10... steps:15020  Discriminator Loss: 0.8953... Generator Loss: 1.2220
Epoch 10/10... steps:15040  Discriminator Loss: 0.7530... Generator Loss: 1.7619
Epoch 10/10... steps:15060  Discriminator Loss: 0.9297... Generator Loss: 1.0896
Epoch 10/10... steps:15080  Discriminator Loss: 1.7776... Generator Loss: 0.4581
Epoch 10/10... steps:15100  Discriminator Loss: 0.8967... Generator Loss: 1.2377
Epoch 10/10... steps:15120  Discriminator Loss: 0.7686... Generator Loss: 2.5736
Epoch 10/10... steps:15140  Discriminator Loss: 0.8243... Generator Loss: 1.3642
Epoch 10/10... steps:15160  Discriminator Loss: 1.0086... Generator Loss: 0.9865
Epoch 10/10... steps:15180  Discriminator Loss: 0.6327... Generator Loss: 1.7730
Epoch 10/10... steps:15200  Discriminator Loss: 0.9128... Generator Loss: 1.1413
Epoch 10/10... steps:15220  Discriminator Loss: 0.7507... Generator Loss: 1.4997
Epoch 10/10... steps:15240  Discriminator Loss: 0.7400... Generator Loss: 1.4347
Epoch 10/10... steps:15260  Discriminator Loss: 0.6823... Generator Loss: 2.0544
Epoch 10/10... steps:15280  Discriminator Loss: 1.1060... Generator Loss: 0.7986
Epoch 10/10... steps:15300  Discriminator Loss: 0.8059... Generator Loss: 1.9527
Epoch 10/10... steps:15320  Discriminator Loss: 1.5808... Generator Loss: 0.5527
Epoch 10/10... steps:15340  Discriminator Loss: 0.7961... Generator Loss: 1.4092
Epoch 10/10... steps:15360  Discriminator Loss: 0.7771... Generator Loss: 1.5140
Epoch 10/10... steps:15380  Discriminator Loss: 0.8946... Generator Loss: 1.2535
Epoch 10/10... steps:15400  Discriminator Loss: 0.6543... Generator Loss: 2.0311
Epoch 10/10... steps:15420  Discriminator Loss: 0.7235... Generator Loss: 1.6817
Epoch 10/10... steps:15440  Discriminator Loss: 0.6660... Generator Loss: 2.2097
Epoch 10/10... steps:15460  Discriminator Loss: 0.9602... Generator Loss: 1.0104
Epoch 10/10... steps:15480  Discriminator Loss: 1.4183... Generator Loss: 2.0445
Epoch 10/10... steps:15500  Discriminator Loss: 1.9779... Generator Loss: 0.8501
Epoch 10/10... steps:15520  Discriminator Loss: 1.3620... Generator Loss: 0.9618
Epoch 10/10... steps:15540  Discriminator Loss: 1.0468... Generator Loss: 2.5838
Epoch 10/10... steps:15560  Discriminator Loss: 0.7244... Generator Loss: 1.7576
Epoch 10/10... steps:15580  Discriminator Loss: 0.9589... Generator Loss: 2.4615
Epoch 10/10... steps:15600  Discriminator Loss: 0.7481... Generator Loss: 2.4373
Epoch 10/10... steps:15620  Discriminator Loss: 0.8703... Generator Loss: 1.2330
Epoch 10/10... steps:15640  Discriminator Loss: 1.4908... Generator Loss: 0.6352
Epoch 10/10... steps:15660  Discriminator Loss: 1.0895... Generator Loss: 0.9567
Epoch 10/10... steps:15680  Discriminator Loss: 0.9288... Generator Loss: 2.0681
Epoch 10/10... steps:15700  Discriminator Loss: 0.7901... Generator Loss: 1.8242
Epoch 10/10... steps:15720  Discriminator Loss: 0.7642... Generator Loss: 1.3835
Epoch 10/10... steps:15740  Discriminator Loss: 0.8407... Generator Loss: 1.8593
Epoch 10/10... steps:15760  Discriminator Loss: 0.6432... Generator Loss: 1.8384
Epoch 10/10... steps:15780  Discriminator Loss: 1.1367... Generator Loss: 0.8096
Epoch 10/10... steps:15800  Discriminator Loss: 0.9381... Generator Loss: 1.0524
Epoch 10/10... steps:15820  Discriminator Loss: 0.5169... Generator Loss: 2.3496
final Discriminator Loss: 0.5169... Generator Loss: 2.3496

提交项目

提交本项目前,确保运行所有 cells 后保存该文件。

保存该文件为 "dlnd_face_generation.ipynb", 并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。